library(Cyclops)
library(BrokenAdaptiveRidge)
## data dimension
p <- 30 # number of covariates
n <- 200 # sample size
## logistic model parameters
itcpt <- 0.2 # intercept
true.beta <- c(1, 0, 0, -1, 1, rep(0, p - 5))
## simulate data from logistic model
set.seed(100)
x <- matrix(rnorm(p * n, mean = 0, sd = 1), ncol = p)
x <- ifelse(abs(x) > 1., 1, 0)
y <- rbinom(n, 1, 1 / (1 + exp(-itcpt - x%*%true.beta)))
# fit BAR model
cyclopsData <- createCyclopsData(y ~ x, modelType = "lr")
barPrior <- createBarPrior(penalty = 0.1, exclude = c("(Intercept)"),
initialRidgeVariance = 1)
cyclopsFit <- fitCyclopsModel(cyclopsData,
prior = barPrior)
fit1 <- coef(cyclopsFit)
# fit BAR using sparse-represented covariates
tmp <- apply(x, 1, function(x) which(x != 0))
y.df <- data.frame(rowId = 1:n, y = y)
x.df <- data.frame(rowId = rep(1:n, lengths(tmp)), covariateId = unlist(tmp), covariateValue = 1)
cyclopsData <- convertToCyclopsData(outcomes = y.df, covariates = x.df, modelType = "lr")
barPrior <- createFastBarPrior(penalty = 0.1, exclude = c("(Intercept)"),
initialRidgeVariance = 1)
fit2 <- coef(cyclopsFit)
# fit BAR using cyclic algorithm
cyclopsData <- createCyclopsData(y ~ x, modelType = "lr")
barPrior <- createFastBarPrior(penalty = 0.1, exclude = c("(Intercept)"),
initialRidgeVariance = 1)
cyclopsFit <- fitCyclopsModel(cyclopsData,
prior = barPrior)
fit3 <- coef(cyclopsFit)
fit1
fit2
fit3
To install the latest stable version, install from CRAN:
install.packages("BrokenAdaptiveRidge")
Documentation can be found on the package website.
PDF versions of the documentation are also available: